The latest Apache Ignite release includes many new features, such as expanded thin client support, improved cluster monitoring, and enhanced cluster self-tuning. Join Vladimir as he discusses and demonstrates the key improvements that Apache Ignite 2.10 offers:

Apache Ignite and Spark are complementary in-memory computing solutions that can be used together to achieve superior performance and functionality to process SQL data.

Spring is a popular framework, and Apache Ignite is a fast layer for data storage. Adding Ignite enables you to manage http sessions, cache-application data, and so on. During this 1.5 hour session, Semyon uses the Spring framework to create a simple web application and demonstrates how Apache Ignite can empower the application. He walks you through steps such as the following:

Apache Ignite’s ANSI-99 SQL support provides application developers a classical SQL database experience while enabling in-memory speeds at a petabyte scale for a variety of workloads. Concise SQL syntax and availability of JDBC and ODBC drivers shields the complexity of Ignite’s distributed architecture from developers and allows them to easily manage and query distributed datasets.

The latest release of the GridGain in-memory computing platform features enhanced support for the platform’s multi-tier database engine, that scales up and out across memory and disk. The changes enable customers to leverage the disk tier of the database engine to query much larger data sets, reduce cost of ownership, and secure sensitive or personal data at rest.

During this webinar, we review various approaches to storing and using authentication data in distributed applications. Moving from the simplest to the most complex models, we consider the pros and cons of each approach. We give special attention to one of the most popular approaches to distributed sessions—single sign-on.

GridGain Nebula, a managed services offering (MSO) for Apache Ignite and GridGain, can offload the management of your in-memory computing environment to provide maximum reliability at a fraction of the cost of staffing an internal team.

The GridGain Operator for Kubernetes enables you to deploy and manage Apache Ignite and GridGain clusters efficiently. The automation that Kubernetes and the Operator provide simplifies provisioning and minimizes the operational and management burden.

Typically, operations exceed fifty percent of the cost of an IT system’s life cycle. By developing applications that can be easily managed, developers can significantly reduce the cost of ownership. Manageability is especially important for distributed applications because they are especially complex and often mission-critical.

Kafka with Debezium and GridGain connectors enables change data capture (CDC) based synchronization between third-party databases and GridGain clusters. Synchronization that is based CDC does not require coding; all it requires is to prepare configuration files for each of the points.

Apache Ignite can scale horizontally to accommodate the data that your applications and services generate. However, in practice, most of us cannot scale out a cluster instantly.

Serverless computing allows you to design and build scalable cloud-native applications without thinking about infrastructure provisioning and orchestration. With Apache Ignite, you can bootstrap an in-memory cluster in the cloud and access data 100-1000x faster than with disk-based databases.

Join this webinar to get started with an Apache Ignite as a Digital Integration Hub for real-time data access across data sources and applications.

This webinar will introduce you to the role of the networking layer in distributed systems with Apache Ignite as an example. You will gain practical insights and learn how to maximize the performance and reliability of your applications running on distributed systems.

Debugging distributed-system applications can be more complex than debugging traditional monolithic applications. As API calls jump across nodes in the cluster, it can be tricky to follow the execution just by analyzing application logs. Tracing adds a useful tool to the root-cause-analysis toolbox.

Growing customer traffic, the launch of new services, seasonal traffic spikes or a host of other load-related issues can slow down customer-facing applications, resulting in poor customer experiences. In-memory computing offers a solution to overcome performance-related challenges and deliver outstanding customer interactions.

Apache Ignite is an excellent tool for external RDBMS, NoSQL or Hadoop database acceleration and offloading. The Ignite in-memory computing platform can power real-time applications that need to process terabytes of data with in-memory speed. Join us for this webinar to learn about the various Ignite deployment options for database acceleration including:

Join us for a special webinar presented by Branimir Angelov, Co-Founder and CTO of Kubo, Software Architecture Consultant in Obecto, and Member of the Comrade Cooperative.

Many machine learning (ML) and deep learning (DL) platforms are slow in production environments. It can sometimes take hours or days to update ML models. This is a result of having the ML processing run on a different system from the operational transactions system in order to avoid a performance degradation.

In this webinar, Glenn Wiebe, GridGain Solution Architect, will introduce developers to Apache Ignite as an in-memory database (IMDB). He will review how an IMDB differs from a cache or an in-memory data grid (IMDG). He will discuss the key characteristics of an IMDB and also highlight core Ignite features and facilities.

Managing distributed systems can be complex and time consuming because there are numerous moving parts. Having a consistent set of tools can give a clear picture of how clusters are performing, when to take action and avoid potential problems, and how to optimize configurations.

Mythili Venkatakrishnan, IBM Distinguished Engineer, will discuss increasing market pressures in key industries such as banking which are leading Z platform users to leverage Digital Integration Hubs for flexible information flow between core transactional systems and hybrid cloud environments.

If your company is in an industry such as ecommerce, logistics, online learning, food delivery, or online business collaboration, you may be seeing a huge spike in your business which is straining the limits of your customer-facing or internal applications. If you need to speed up and scale out your applications, one of the fastest approaches is to deploy an in-memory data grid.

Join Denis Magda, Apache Ignite veteran and GridGain Head of Developer Relations, as he demonstrates various Apache Ignite capabilities you might have heard of or read about. Topics covered will include clustering, compute and SQL capabilities, data loading and streaming.

Apache Ignite’s ANSI-99 SQL support provides application developers a classical SQL database experience while enabling in-memory speeds at petabyte scale for a variety of workloads. Concise SQL syntax and availability of JDBC and ODBC drivers shields the complexity of Ignite’s distributed architecture from developers and allows them to easily manage and query distributed datasets.

When working with multiple data centers, it is important to ensure high availability of your GridGain cluster. The GridGain Enterprise and Ultimate Editions, built on Apache Ignite®, include a Data Center Replication feature that allows data transfer between caches in distinct topologies, even located in different geographic locations.

The Apache Ignite transactional engine can execute distributed ACID transactions which span multiple nodes, data partitions, and caches/tables. This key-value API differs slightly from traditional SQL-based transactions but its reliability and flexibility lets you achieve an optimal balance between consistency and performance at scale by following several guidelines.

Apache Ignite can function in a strong consistency mode which keeps application records in sync across all primary and backup replicas. It also supports distributed ACID transactions that allow you to update multiple entries stored on different cluster nodes and in various caches/tables.

Apache Ignite 2.8 includes over 1,900 upgrades and fixes that enhance almost all components of the platform. The release notes include hundreds of line items cataloging the improvements. In this webinar Ignite community members demonstrate and dissect new capabilities related to production maintenance, monitoring, and machine learning including:

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms (IMCPs) in this Apache Ignite tutorial. IMCPs boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines.